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LightGBM on dimension reduced dataset
Kamil A. Kaczmarek edited this page Jul 10, 2018
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4 revisions
- truncated svd projection
truncated_svd__n_components: 50
truncated_svd__n_iter: 10
- pca projection
pca__n_components: 100
- fast ica projection
fast_ica__n_components: 15
- factor analysis
factor_analysis__n_components: 50
- gaussian random projection
gaussian_random_projection__n_components: 50
gaussian_projection__eps: 0.1
Note as it turns out the eps
parameter doesn't matter (tried 0.01,0.1,1.0) with exact same results
- sparse random projection
sparse_random_projection__n_components: 50
model | CV | LB 🏆 |
---|---|---|
lightGBM truncated svd | 1.56 | |
lightGBM pca | 1.55 | |
lightGBM fast ica | 1.57 | |
lightGBM factor analysis | 1.51 | |
lightGBM gaussian random projection | 1.63 | |
lightGBM sparse random projection | 1.47 | |
lightGBM projections (all) | 1.47 | |
lightGBM projections best (sparse random projection + factor analysis + truncated svd + fast-ica) | 1.448 | |
lightGBM projections second best (sparse random projection) | 1.452 | |
lightGBM raw + projections (second best) | 1.393 | |
lightGBM projections (second best) + aggregations | 1.345 | |
lightGBM raw + projections (second best) + aggregations | 1.3416 | 1.41 🚀 |
check our GitHub organization https://github.com/neptune-ml for more cool stuff 😃
Kamil & Kuba, core contributors
- honey bee 🐝 LightGBM and 5fold CV
- beetle 🪲 LightGBM on binarized dataset
- dromedary camel 🐪 LightGBM with row aggregations
- whale 🐳 LightGBM on dimension reduced dataset
- water buffalo 🐃 Exploring various dimension reduction techniques
- blowfish 🐡 bucketing row aggregations